ABForge is a post-training pipeline for paper-grounded ablation design. This checkpoint is trained with GRPO directly from Qwen/Qwen3-8B (no supervised warm-start), optimizing a fixed rubric-based reward (GRPO).

Task

Given the ablation-free context of a research paper, the model proposes candidate ablation objectives, each expressed as a Target Module (the component to ablate) paired with a Research Question it is meant to answer.

Training data

GRPO on train/RL_task1_30K.jsonl, from SlowGuess/abforge-data (derived from CC-licensed research papers). Evaluation uses the held-out AblationBench split (eval/ablationbench_200.jsonl) of the same dataset.

Related models (Task 1)

Evaluation

Reproduce AblationBench evaluation with the SlowGuess/Abforge_1 code:

bash

git clone https://github.com/SlowGuess/Abforge_1 && cd Abforge_1
huggingface-cli download SlowGuess/abforge-data --repo-type dataset --local-dir data

export MODEL_PATH=SlowGuess/ABForge-Qwen3-8B-Task1-RL

# 1. Generate predictions on AblationBench (writes scorer-ready JSONL)
python run_inference_local.py --task 1 \
  --input data/eval/ablationbench_200.jsonl \
  --output preds.jsonl \
  --model-path "$MODEL_PATH" --dtype bf16 --device-map auto \
  --max-new-tokens 5120 --temperature 0.0 --stop-on '</Result>'

# 2. Score with an OpenAI-compatible judge
export JUDGE_API_BASE=... JUDGE_API_KEY=... JUDGE_MODEL=...
scripts/evaluate_task1.sh preds.jsonl

Citation

bibtex

@misc{abforge,
  title  = {ABForge: A Post-Training Pipeline for Paper-Grounded Ablation Design},
  author = {ABForge authors},
  year   = {2026},
  howpublished = {\url{https://github.com/SlowGuess/Abforge_1}}
}

SlowGuess/ABForge-Qwen3-8B-Task1-RL

ABForge-Qwen3-8B-Task1-RL

Task

Training data

Related models (Task 1)

Evaluation

Links

Citation

SlowGuess/
ABForge-Qwen3-8B-Task1-RL